Shelf Label Detection via Image Recognition Correction
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Solution Overview
Problem
In the retail industry, accurately monitoring stock quantities on display shelves is challenging due to inadequate setting of monitoring areas, leading to discrepancies between physical and theoretical stock, which requires manual correction and can result in lost sales opportunities and negative customer perceptions.
Innovation Solution
A shelf label detection device and method that uses camera video to obtain and correct shelf label positions, determining accurate positions through video recognition and shelf allocation information to automate the monitoring of stock quantities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual visual inspection is used to monitor stock quantity, then accuracy of stock monitoring can be improved, but labor cost and time consumption increase
Solution Approach 1:
The patent replaces manual visual inspection with an automated image recognition system that captures shelf images, detects product presence, and monitors stock levels automatically. This substitution eliminates the need for manual correction while maintaining monitoring accuracy.
Solution Approach 2:
The system enables self-service stock monitoring where the shelf management system automatically detects stock levels through image recognition and generates alerts when replenishment is needed, without requiring manual intervention for each monitoring cycle.
2Productivity
If automated stock monitoring system is implemented, then productivity can be improved, but system complexity increases
Solution Approach 1:
The patent integrates multiple functions into a single shelf management system that combines image capture, image recognition, stock level determination, and alert generation. This multi-functionality improves productivity while managing complexity through integration rather than separate systems.
Solution Approach 2:
The system uses an image recognition server as an intermediary that processes images captured by shelf management devices and returns stock level information. This intermediary approach simplifies the overall system architecture by centralizing the complex recognition logic in a dedicated server.
3Extent of automation
If shelf label position is determined only by video recognition, then automation can be improved, but detection accuracy decreases
Solution Approach 1:
The patent employs feedback mechanisms where the system learns from previously identified shelf label positions and uses this information to improve subsequent detections. The image recognition server accumulates data about shelf configurations and uses this feedback to enhance detection accuracy over time.
Solution Approach 2:
The system performs preliminary identification of shelf label positions during initial system setup or during periods when the shelf is known to be properly configured. These pre-identified positions serve as reference data that guides and improves real-time automated detection accuracy.
Data Source
AI summary
The present disclosure accurately detects the position of a shelf label disposed on a display shelf. A shelf label detection device may be provided with a shelf label position correction unit and a shelf label position identification unit. The shelf label position correction unit corrects a manual shelf label position set including a shelf label position that has been set in advance for a reference camera image, using an automatic shelf label position set which includes a shelf label position detected from a monitoring camera image using image recognition, thereby generating a corrected shelf label position set. The shelf label position identification unit uses the corrected shelf label position set to identify the shelf label position in the monitoring camera image.